# What is an AI agent? Definition and business implications

> An AI agent is an autonomous system that combines a language model with external tools to execute multi-step tasks: it analyses a request, plans an action sequence, executes, observes, and adapts its strategy. It differs from an assistant that merely responds.

## Definition
Three characteristics distinguish an agent from a simple assistant. Autonomy: the agent decides itself which tools to use and in what order, without preprogrammed script. Planning: for a complex task, the agent decomposes into sub-tasks that it chains and revises. The observation-action loop: the agent acts, observes the result of its action, and adapts its strategy accordingly (the ReAct cycle, *Reasoning + Acting*). According to the Anthropic survey published in December 2025 (500+ technical leaders), 57% of organisations deploy agents for multi-step workflows, and 81% plan to tackle more complex use cases in 2026. Dominant frameworks: LangGraph, CrewAI, AutoGen, and the Claude Agent SDK. Three major use cases are emerging: software development agents, customer service agents, data analysis agents.

## Example
Illustrative case. A 600-employee logistics mid-cap deployed in 2025 an agent to handle customer complaints. The agent receives a complaint email, identifies the order number, queries the ERP to retrieve the status, cross-references with the carrier data, proposes a decision (credit, return, commercial gesture), submits it to the manager if it exceeds 200 euros, and sends the response to the customer. Before the agent, this processing took 18 minutes per case on average. With the agent, the time drops to 4 minutes for 92% of cases, the remaining 8% being escalated to a human operator. Over the year, 11 full-time equivalents could be reassigned to higher-value commercial tasks.

## For a leader
An AI agent is not a more powerful assistant: it is an operational paradigm shift. Three implications for the executive. First, business processes candidates for agents are not the same as for classical assistants. Look for existing multi-step workflows: case processing, ticket resolution, complete analysis. Single-step processes do not need an agent. Second, agents in production require specific supervision infrastructure: according to the LangChain survey (1,340 respondents, late 2025), 89% of organisations have implemented some form of observability for their agents, and 94% of those with agents in production. Without structured logs of the agent's decisions, you cannot audit or correct it. Third, the technical leaders surveyed by Anthropic cite as main obstacles integration with existing systems (46%), data access and quality (42%) and change management (39%). The model is not the only issue: the application chain matters as much.

## FAQ

**What distinguishes an AI agent from an assistant?**
Three things. Autonomy: the agent decides which tools to use and in what order, with no script. Planning: it breaks a complex task into sub-tasks that it chains and revises. The observe-act loop (ReAct cycle): it acts, observes the result, and adapts. An assistant simply answers.

**Are AI agents actually deployed in companies?**
Yes. According to an Anthropic survey from December 2025 (more than 500 technical decision-makers), 57% of organisations deploy agents on multi-step workflows and 81% plan more complex use cases in 2026. Common frameworks: LangGraph, CrewAI, AutoGen, Claude Agent SDK.

**Which processes suit an agent rather than an assistant?**
Existing multi-step workflows, not simple questions and answers. A good candidate chains several actions across real systems: receive a request, query an ERP, cross-check data, propose a decision, send it for approval above a threshold, then reply. That is where an agent's autonomy creates value, with human control over high-stakes decisions.

## See also

- [MCP (Model Context Protocol)](https://wyp.agency/en/glossary/mcp/)
- [Function calling](https://wyp.agency/en/glossary/function-calling/)
- [AI workflow](https://wyp.agency/en/glossary/ai-workflow/)
- [Agentic loop](https://wyp.agency/en/glossary/agentic-loop/)
- [Claude Code](https://wyp.agency/en/glossary/claude-code/)
- [Delegating to AI agents](https://wyp.agency/en/glossary/delegating-to-ai-agents/)
- [Agentic AI](https://wyp.agency/en/glossary/agentic-ai/)
- [A2A (Agent2Agent)](https://wyp.agency/en/glossary/a2a/)
- [Prompt injection](https://wyp.agency/en/glossary/prompt-injection/)
- [Chatbot (conversational agent)](https://wyp.agency/en/glossary/chatbot/)

## Sources

- How enterprises are building AI agents in 2026, Anthropic, 9 December 2025 (survey run with Material among 500+ technical leaders) (https://claude.com/blog/how-enterprises-are-building-ai-agents-in-2026)
- State of Agent Engineering, LangChain, survey of 1,340 professionals, fielded 18 November to 2 December 2025 (https://www.langchain.com/state-of-agent-engineering)

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Entry from the WYP.agency AI glossary · https://wyp.agency/en/glossary/ai-agent/ · Last updated : 2026-10-14